OpenRaiser/Intern-Atlas huggingface dataset:
Build a Compact Binary Neural Network through Bit-level Sensitivity and Data Pruning
Venue: arXiv cs.CV 2018
Year: 2018

Convolutional neural network (CNN) has been widely used for vision-based tasks. Due to the high computational complexity and memory storage requirement, it is hard to directly deploy a full-precision CNN on embedded devices. The hardware-friendly designs are needed for re-source-limited and energy-constrained embed-ded devices. Emerging solutions are adopted for the neural network compression, e.g., bina-ry/ternary weight network, pruned network and quantized network. Among them, Binarized Neural Network (BNN) is believed to be the most hardware-friendly framework due to its small network size and low computational com-plexity. No existing work has further shrunk the size of BNN. In this work, we explore the redun-dancy in BNN and build a compact BNN (CBNN) based on the bit-level sensitivity analy-sis and bit-level data pruning. The input data is converted to a high dimensional bit-sliced for-mat. In post-training stage, we analyze the im-pact of different bit slices to the accuracy. By pruning the redundant input bit slices and shrinking the network size, we are able to build a more compact BNN. Our result shows that we can further scale down the network size of the BNN up to 3.9x with no more than 1% accuracy drop. The actual runtime can be reduced up to 2x and 9.9x compared with the baseline BNN and its full-precision counterpart, respectively.
GeniePath: Graph Neural Networks with Adaptive Receptive Paths
Venue: arXiv cs.LG 2018
Year: 2018

We present, GeniePath, a scalable approach for learning adaptive receptive fields of neural networks defined on permutation invariant graph data. In GeniePath, we propose an adaptive path layer consists of two complementary functions designed for breadth and depth exploration respectively, where the former learns the importance of different sized neighborhoods, while the latter extracts and filters signals aggregated from neighbors of different hops away. Our method works in both transductive and inductive settings, and extensive experiments compared with competitive methods show that our approaches yield state-of-the-art results on large graphs.
On OTFS Modulation for High-Doppler Fading Channels
Venue: arXiv cs.IT 2018
Year: 2018

Orthogonal time frequency space (OTFS) modulation is a 2-dimensional (2D) modulation scheme designed in the delay-Doppler domain, unlike traditional modulation schemes which are designed in the time-frequency domain. Through a series of 2D transformations, OTFS converts a doubly-dispersive channel into an almost non-fading channel in the delay-Doppler domain. In this domain, each symbol in a frame experiences an almost constant fade, thus achieving significant performance gains over existing modulation schemes such as OFDM. The sparse delay-Doppler impulse response which reflects the actual physical geometry of the wireless channel enables efficient channel estimation, especially in high-Doppler fading channels. This paper investigates OTFS from a signal detection and channel estimation perspective, and proposes a Markov chain Monte-Carlo sampling based detection scheme and a pseudo-random noise (PN) pilot based channel estimation scheme in the delay-Doppler domain.
Resset: A Recurrent Model for Sequence of Sets with Applications to Electronic Medical Records
Venue: arXiv cs.NE 2018
Year: 2018

Modern healthcare is ripe for disruption by AI. A game changer would be automatic understanding the latent processes from electronic medical records, which are being collected for billions of people worldwide. However, these healthcare processes are complicated by the interaction between at least three dynamic components: the illness which involves multiple diseases, the care which involves multiple treatments, and the recording practice which is biased and erroneous. Existing methods are inadequate in capturing the dynamic structure of care. We propose Resset, an end-to-end recurrent model that reads medical record and predicts future risk. The model adopts the algebraic view in that discrete medical objects are embedded into continuous vectors lying in the same space. We formulate the problem as modeling sequences of sets, a novel setting that have rarely, if not, been addressed. Within Resset, the bag of diseases recorded at each clinic visit is modeled as function of sets. The same hold for the bag of treatments. The interaction between the disease bag and the treatment bag at a visit is modeled in several, one of which as residual of diseases minus the treatments. Finally, the health trajectory, which is a sequence of visits, is modeled using a recurrent neural network. We report results on over a hundred thousand hospital visits by patients suffered from two costly chronic diseases -- diabetes and mental health. Resset shows promises in multiple predictive tasks such as readmission prediction, treatments recommendation and diseases progression.
Scheduling and Checkpointing optimization algorithm for Byzantine fault tolerance in Cloud Clusters
Venue: arXiv cs.DC 2018
Year: 2018

Among those faults Byzantine faults offers serious challenge to fault tolerance mechanism, because it often go undetected at the initial stage and it can easily propagate to other VMs before a detection is made. Consequently some of the mission critical application such as air traffic control, online baking etc still staying away from the cloud for such reasons. However if a Byzantine faults is not detected and tolerated at initial stage then applications such as big data analytics can go completely wrong in spite of hours of computations performed by the entire cloud. Therefore in the previous work a fool-proof Byzantine fault detection has been proposed, as a continuation this work designs a scheduling algorithm (WSSS) and checkpoint optimization algorithm (TCC) to tolerate and eliminate the Byzantine faults before it makes any impact. The WSSS algorithm keeps track of server performance which is part of Virtual Clusters to help allocate best performing server to mission critical application. WSSS therefore ranks the servers based on a counter which monitors every Virtual Nodes (VN) for time and performance failures. The TCC algorithm works to generalize the possible Byzantine error prone region through monitoring delay variation to start new VNs with previous checkpointing. Moreover it can stretch the state interval for performing and error free VNs in an effect to minimize the space, time and cost overheads caused by checkpointing. The analysis is performed with plotting state transition and CloudSim based simulation. The result shows TCC reduces fault tolerance overhead exponentially and the WSSS allots virtual resources effectively
Pose Flow: Efficient Online Pose Tracking
Venue: arXiv cs.CV 2018
Year: 2018

Multi-person articulated pose tracking in unconstrained videos is an important while challenging problem. In this paper, going along the road of top-down approaches, we propose a decent and efficient pose tracker based on pose flows. First, we design an online optimization framework to build the association of cross-frame poses and form pose flows (PF-Builder). Second, a novel pose flow non-maximum suppression (PF-NMS) is designed to robustly reduce redundant pose flows and re-link temporal disjoint ones. Extensive experiments show that our method significantly outperforms best-reported results on two standard Pose Tracking datasets by 13 mAP 25 MOTA and 6 mAP 3 MOTA respectively. Moreover, in the case of working on detected poses in individual frames, the extra computation of pose tracker is very minor, guaranteeing online 10FPS tracking. Our source codes are made publicly available(https://github.com/YuliangXiu/PoseFlow).
Modeling Text with Graph Convolutional Network for Cross-Modal Information Retrieval
Venue: arXiv cs.IR 2018
Year: 2018

Cross-modal information retrieval aims to find heterogeneous data of various modalities from a given query of one modality. The main challenge is to map different modalities into a common semantic space, in which distance between concepts in different modalities can be well modeled. For cross-modal information retrieval between images and texts, existing work mostly uses off-the-shelf Convolutional Neural Network (CNN) for image feature extraction. For texts, word-level features such as bag-of-words or word2vec are employed to build deep learning models to represent texts. Besides word-level semantics, the semantic relations between words are also informative but less explored. In this paper, we model texts by graphs using similarity measure based on word2vec. A dual-path neural network model is proposed for couple feature learning in cross-modal information retrieval. One path utilizes Graph Convolutional Network (GCN) for text modeling based on graph representations. The other path uses a neural network with layers of nonlinearities for image modeling based on off-the-shelf features. The model is trained by a pairwise similarity loss function to maximize the similarity of relevant text-image pairs and minimize the similarity of irrelevant pairs. Experimental results show that the proposed model outperforms the state-of-the-art methods significantly, with 17% improvement on accuracy for the best case.
Plan Explanations as Model Reconciliation -- An Empirical Study
Venue: arXiv cs.AI 2018
Year: 2018

Recent work in explanation generation for decision making agents has looked at how unexplained behavior of autonomous systems can be understood in terms of differences in the model of the system and the human's understanding of the same, and how the explanation process as a result of this mismatch can be then seen as a process of reconciliation of these models. Existing algorithms in such settings, while having been built on contrastive, selective and social properties of explanations as studied extensively in the psychology literature, have not, to the best of our knowledge, been evaluated in settings with actual humans in the loop. As such, the applicability of such explanations to human-AI and human-robot interactions remains suspect. In this paper, we set out to evaluate these explanation generation algorithms in a series of studies in a mock search and rescue scenario with an internal semi-autonomous robot and an external human commander. We demonstrate to what extent the properties of these algorithms hold as they are evaluated by humans, and how the dynamics of trust between the human and the robot evolve during the process of these interactions.
JobPruner: A Machine Learning Assistant for Exploring Parameter Spaces in HPC Applications
Venue: arXiv cs.DC 2018
Year: 2018

High Performance Computing (HPC) applications are essential for scientists and engineers to create and understand models and their properties. These professionals depend on the execution of large sets of computational jobs that explore combinations of parameter values. Avoiding the execution of unnecessary jobs brings not only speed to these experiments, but also reductions in infrastructure usage---particularly important due to the shift of these applications to HPC cloud platforms. Our hypothesis is that data generated by these experiments can help users in identifying such jobs. To address this hypothesis we need to understand the similarity levels among multiple experiments necessary for job elimination decisions and the steps required to automate this process. In this paper we present a study and a machine learning-based tool called JobPruner to support parameter exploration in HPC experiments. The tool was evaluated with three real-world use cases from different domains including seismic analysis and agronomy. We observed the tool reduced 93% of jobs in a single experiment, while improving quality in most scenarios. In addition, reduction in job executions was possible even considering past experiments with low correlations.
Searching for Representative Modes on Hypergraphs for Robust Geometric Model Fitting
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we propose a simple and effective {geometric} model fitting method to fit and segment multi-structure data even in the presence of severe outliers. We cast the task of geometric model fitting as a representative mode-seeking problem on hypergraphs. Specifically, a hypergraph is firstly constructed, where the vertices represent model hypotheses and the hyperedges denote data points. The hypergraph involves higher-order similarities (instead of pairwise similarities used on a simple graph), and it can characterize complex relationships between model hypotheses and data points. {In addition, we develop a hypergraph reduction technique to remove "insignificant" vertices while retaining as many "significant" vertices as possible in the hypergraph}. Based on the {simplified hypergraph, we then propose a novel mode-seeking algorithm to search for representative modes within reasonable time. Finally, the} proposed mode-seeking algorithm detects modes according to two key elements, i.e., the weighting scores of vertices and the similarity analysis between vertices. Overall, the proposed fitting method is able to efficiently and effectively estimate the number and the parameters of model instances in the data simultaneously. Experimental results demonstrate that the proposed method achieves significant superiority over {several} state-of-the-art model fitting methods on both synthetic data and real images.
Secure Range Queries for Multiple Users
Venue: arXiv cs.CR 2018
Year: 2018

Order-preserving encryption allows encrypting data, while still enabling efficient range queries on the encrypted data. Moreover, it does not require any change to the database management system, because comparison operates on ciphertexts as on plaintexts. This makes order-preserving encryption schemes very suitable for data outsourcing in cloud computing scenarios. However, all order-preserving encryption schemes are necessarily symmetric limiting the use case to one client and one server. Imagine a scenario where a Data Owner encrypts its data before outsourcing it to the Cloud Service Provider and a Data Analyst wants to execute private range queries on this data. This scenario occurs in many cases of collaborative machine learning where data source and processor are different entities. Then either the Data Owner must reveal its encryption key or the Data Analyst must reveal the private queries. In this paper, we overcome this limitation by allowing the equivalent of a public-key order-preserving encryption. We present a secure multiparty protocol that enables secure range queries for multiple users. In this scheme, the Data Analyst cooperates with the Data Owner and the Cloud Service Provider in order to order-preserving encrypt the private range queries without revealing any other information to the parties. We implemented our scheme and observed that if the database size of the Data Owner has 1 million entries it takes only about 0.3 s on average via a loopback interface (1.3 s via a LAN) to encrypt an input of the Data Analyst.
Human Action Adverb Recognition: ADHA Dataset and A Three-Stream Hybrid Model
Venue: arXiv cs.CV 2018
Year: 2018

We introduce the first benchmark for a new problem --- recognizing human action adverbs (HAA): "Adverbs Describing Human Actions" (ADHA). This is the first step for computer vision to change over from pattern recognition to real AI. We demonstrate some key features of ADHA: a semantically complete set of adverbs describing human actions, a set of common, describable human actions, and an exhaustive labeling of simultaneously emerging actions in each video. We commit an in-depth analysis on the implementation of current effective models in action recognition and image captioning on adverb recognition, and the results show that such methods are unsatisfactory. Moreover, we propose a novel three-stream hybrid model to deal the HAA problem, which achieves a better result.
Anatomy of Leadership in Collective Behaviour
Venue: arXiv cs.MA 2018
Year: 2018

Understanding the mechanics behind the coordinated movement of mobile animal groups (collective motion) provides key insights into their biology and ecology, while also yielding algorithms for bio-inspired technologies and autonomous systems. It is becoming increasingly clear that many mobile animal groups are composed of heterogeneous individuals with differential levels and types of influence over group behaviors. The ability to infer this differential influence, or leadership, is critical to understanding group functioning in these collective animal systems. Due to the broad interpretation of leadership, many different measures and mathematical tools are used to describe and infer "leadership", e.g., position, causality, influence, information flow. But a key question remains: which, if any, of these concepts actually describes leadership? We argue that instead of asserting a single definition or notion of leadership, the complex interaction rules and dynamics typical of a group implies that leadership itself is not merely a binary classification (leader or follower), but rather, a complex combination of many different components. In this paper we develop an anatomy of leadership, identify several principle components and provide a general mathematical framework for discussing leadership. With the intricacies of this taxonomy in mind we present a set of leadership-oriented toy models that should be used as a proving ground for leadership inference methods going forward. We believe this multifaceted approach to leadership will enable a broader understanding of leadership and its inference from data in mobile animal groups and beyond.
Counting and Sampling from Markov Equivalent DAGs Using Clique Trees
Venue: arXiv cs.DS 2018
Year: 2018

A directed acyclic graph (DAG) is the most common graphical model for representing causal relationships among a set of variables. When restricted to using only observational data, the structure of the ground truth DAG is identifiable only up to Markov equivalence, based on conditional independence relations among the variables. Therefore, the number of DAGs equivalent to the ground truth DAG is an indicator of the causal complexity of the underlying structure--roughly speaking, it shows how many interventions or how much additional information is further needed to recover the underlying DAG. In this paper, we propose a new technique for counting the number of DAGs in a Markov equivalence class. Our approach is based on the clique tree representation of chordal graphs. We show that in the case of bounded degree graphs, the proposed algorithm is polynomial time. We further demonstrate that this technique can be utilized for uniform sampling from a Markov equivalence class, which provides a stochastic way to enumerate DAGs in the equivalence class and may be needed for finding the best DAG or for causal inference given the equivalence class as input. We also extend our counting and sampling method to the case where prior knowledge about the underlying DAG is available, and present applications of this extension in causal experiment design and estimating the causal effect of joint interventions.
Face recognition for monitoring operator shift in railways
Venue: arXiv cs.CV 2018
Year: 2018

Train Pilot is a very tedious and stressful job. Pilots must be vigilant at all times and its easy for them to lose track of time of shift. In countries like USA the pilots are mandated by law to adhere to 8 hour shifts. If they exceed 8 hours of shift the railroads may be penalized for over-tiring their drivers. The problem happens when the 8 hour shift may end in middle of a journey. In such case, the new drivers must be moved to the location locomotive is operating for shift change. Hence accurate monitoring of drivers during their shift and making sure the shifts are scheduled correctly is very important for railroads. Here we propose an automated camera system that uses camera mounted inside Locomotive cabs to continuously record video feeds. These feeds are analyzed in real time to detect the face of driver and recognize the driver using state of the art deep Learning techniques. The outcome is an increased safety of train pilots. Cameras continuously capture video from inside the cab which is stored on an on board data acquisition device. Using advanced computer vision and deep learning techniques the videos are analyzed at regular intervals to detect presence of the pilot and identify the pilot. Using a time based analysis, it is identified for how long that shift has been active. If this time exceeds allocated shift time an alert is sent to the dispatch to adjust shift hours.
Deep Neural Network-based Cooperative Visual Tracking through Multiple Micro Aerial Vehicles
Venue: arXiv cs.RO 2018
Year: 2018

Multi-camera full-body pose capture of humans and animals in outdoor environments is a highly challenging problem. Our approach to it involves a team of cooperating micro aerial vehicles (MAVs) with on-board cameras only. The key enabling-aspect of our approach is the on-board person detection and tracking method. Recent state-of-the-art methods based on deep neural networks (DNN) are highly promising in this context. However, real time DNNs are severely constrained in input data dimensions, in contrast to available camera resolutions. Therefore, DNNs often fail at objects with small scale or far away from the camera, which are typical characteristics of a scenario with aerial robots. Thus, the core problem addressed in this paper is how to achieve on-board, real-time, continuous and accurate vision-based detections using DNNs for visual person tracking through MAVs. Our solution leverages cooperation among multiple MAVs. First, each MAV fuses its own detections with those obtained by other MAVs to perform cooperative visual tracking. This allows for predicting future poses of the tracked person, which are used to selectively process only the relevant regions of future images, even at high resolutions. Consequently, using our DNN-based detector we are able to continuously track even distant humans with high accuracy and speed. We demonstrate the efficiency of our approach through real robot experiments involving two aerial robots tracking a person, while maintaining an active perception-driven formation. Our solution runs fully on-board our MAV's CPU and GPU, with no remote processing. ROS-based source code is provided for the benefit of the community.
Intent-Based In-flight Service Encryption in Multi-Layer Transport Networks
Venue: arXiv cs.NI 2018
Year: 2018

We demonstrate multi-layer encrypted service provisioning via the ACINO orchestrator. ACINO combines a novel intent interface with an ONOS-based SDN orchestrator to facilitate encrypted services at IP, Ethernet and optical network layers.
Comparing approaches for mitigating intergroup variability in personality recognition
Venue: arXiv cs.SD 2018
Year: 2018

Personality have been found to predict many life outcomes, and there have been huge interests on automatic personality recognition from a speaker's utterance. Previously, we achieved accuracies between 37%-44% for three-way classification of high, medium or low for each of the Big Five personality traits (Openness to Experience, Conscientiousness, Extraversion, Agreeableness, Neuroticism). We show here that we can improve performance on this task by accounting for heterogeneity of gender and L1 in our data, which has English speech from female and male native speakers of Chinese and Standard American English (SAE). We experiment with personalizing models by L1 and gender and normalizing features by speaker, L1 group, and/or gender.